73 lines
2.9 KiB
Markdown
73 lines
2.9 KiB
Markdown
---
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title: t-SNE's Data Visualization
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short_title: t-SNE Visualization
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description: Data visualizaiton with t-SNE with higher dimensional data.
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category: Tuning & Training
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weight: 10
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---
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## t-SNE's Data Visualization
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[t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) (t-SNE) is a data-visualization tool created by Laurens van der Maaten at Delft University of Technology.
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While it can be used for any data, t-SNE (pronounced Tee-Snee) is only really meaningful with labeled data, which clarify how the input is clustering. Below, you can see the kind of graphic you can generate in DL4J with t-SNE working on MNIST data.
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Look closely and you can see the numerals clustered near their likes, alongside the dots.
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Here's how t-SNE appears in Deeplearning4j code.
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```java
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public class TSNEStandardExample {
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private static Logger log = LoggerFactory.getLogger(TSNEStandardExample.class);
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public static void main(String[] args) throws Exception {
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//STEP 1: Initialization
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int iterations = 100;
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//create an n-dimensional array of doubles
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DataTypeUtil.setDTypeForContext(DataBuffer.Type.DOUBLE);
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List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
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//STEP 2: Turn text input into a list of words
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log.info("Load & Vectorize data....");
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File wordFile = new ClassPathResource("words.txt").getFile(); //Open the file
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//Get the data of all unique word vectors
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Pair<InMemoryLookupTable,VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
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VocabCache cache = vectors.getSecond();
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INDArray weights = vectors.getFirst().getSyn0(); //seperate weights of unique words into their own list
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for(int i = 0; i < cache.numWords(); i++) //seperate strings of words into their own list
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cacheList.add(cache.wordAtIndex(i));
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//STEP 3: build a dual-tree tsne to use later
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log.info("Build model....");
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BarnesHutTsne tsne = new BarnesHutTsne.Builder()
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.setMaxIter(iterations).theta(0.5)
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.normalize(false)
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.learningRate(500)
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.useAdaGrad(false)
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// .usePca(false)
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.build();
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//STEP 4: establish the tsne values and save them to a file
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log.info("Store TSNE Coordinates for Plotting....");
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String outputFile = "target/archive-tmp/tsne-standard-coords.csv";
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(new File(outputFile)).getParentFile().mkdirs();
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tsne.plot(weights,2,cacheList,outputFile);
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//This tsne will use the weights of the vectors as its matrix, have two dimensions, use the words strings as
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//labels, and be written to the outputFile created on the previous line
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}
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}
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```
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Here is an image of the tsne-standard-coords.csv file plotted using gnuplot.
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